The field of data compression continues to evolve with innovative techniques that aim to optimize efficiency by leveraging prior knowledge of the source data. This paper explores an approach that incorporates prior knowledge of the source—or of a source correlated to the one being compressed—to significantly enhance compression efficiency. By utilizing this prior knowledge, the compression process can shift from relying solely on the source's entropy to leveraging conditional entropy. This substitution establishes a new theoretical limit for compression, enabling substantial improvements in performance. Interactive data compression involves incorporating a degree of interaction between the compressor and the decompressor, especially when data transmission is involved. This interaction facilitates the more effective use of shared prior knowledge about the source, resulting in greater compression efficiency. This paper reviews existing studies that have adopted interactive approaches to data compression and examines the potential benefits and challenges associated with these methods. The findings highlight the promise of conditional compression in achieving enhanced data transmission efficiency and suggest directions for future research in interactive data compression strategies.